Recently I was dealing with an unexpected issue raised by our Support customer, in which data became inconsistent after a schema change was applied.

After some investigation, it turned out that affected tables had a special word in the comments of some columns, which triggered an already known (and fixed) issue with the TableParser.pm library of Percona Toolkit.  The problem is that the customer was using an outdated Toolkit version, where pt-online-schema-change was using that buggy parser.

This bug applies only to Percona Toolkit versions up to 3.0.10, so if you have already 3.0.11 or newer installed, you can skip the rest of this post as these are no longer affected.

I am writing this post to warn every user of pt-online-schema-change who has not upgraded the toolkit, as …

There is NEW ERA for Galera Cluster monitoring and management and as we just recently released Galera Manager 1.0 into the wild for everyone to deploy their Galera Clusters within an Amazon Web Services (AWS) Elastic Compute Cloud (EC2) environment to achieve MySQL High Availability, Multi-Master MySQL, active-active clustering out of the box on Amazon and Disaster Recovery, all from the comfort of a web-based graphical user interface (GUI).

The Galera Manager is the new graphical user interface (GUI) tool for provisioning Galera Clusters on Amazon Web Services (AWS) Elastic Compute Cloud (EC2). It allows an administrator to easily create and add nodes, without manually having to configure each node. Perhaps more useful is that Galera Manager provides charts for monitoring the host and database metrics, to ensure the proper and efficient functioning of …

Visibility is a blessing, and with databases, visibility is a must. That’s true not only for metrics but for the queries themselves. Having info on all the stats around query execution is priceless, and Percona Monitoring and Management (PMM) offers that in the form of the Query Analytics dashboard (QAN).

But where to start? QAN helps you with that by calculating the query profile. What is the profile? It’s a rank of queries, ordered by Load, so it is easy to spot the heaviest queries hitting your database. The Load is defined as the “Average Active Queries” but can also be defined as a mix of Query Execution Time Plus Query count. In other words, all the time the query was alive and kicking.

The Profile in PMM 2.10.0 looks like this:

The purpose of this profile is to facilitate the task of finding the …

In Use MySQL BLOB column with PHP to store .pdf file, I covered an example of how to store a .pdf file in the actual database table using the MySQL BLOB datatype and PHP. Now that we have .pdf’s stored in the database, how do we display them in the browser? This blog post answers that exact question. Continue reading to see a working example using PHP…

Photo by Ben on Unsplash

Self-Promotion:

If you …

This blog post is referring to the uploaded slides.

DBT2 is based on the standard benchmark TPC-C. This benchmark is a mix of read

and write activity. It is much more write intensive than many other benchmarks.

This has resulted that performance in NDB in previous versions have only scaled

to nodes with around 6 LDM threads. With the introduction of multiple sockets

for communication between nodes we have been able to remove this limitation.

Thus DBT2 serves as a good tool to verify that MySQL Cluster 8.0.20 has improved

its capability to scale writes.

To our disposal for those tests we had access to a set of bare metal servers in

the Oracle Cloud (OCI). The data nodes used DenseIO2 bare metal servers with

…

 This blog is a presentation based on the uploaded slides.

We have focused a lot of energy on improving our capabilities to execute complex

queries in an efficient manner. The first step on these improvements we took in

MySQL Cluster 7.6, but even more improvements have been made in

MySQL Cluster 8.0.

There are four types of improvements that we have made. One is that we have added

the option to use a shared memory transporter between the MySQL Server and the

NDB data nodes when they are located on the same machine.

The second improvement is the adaptive CPU spinning described in blog post 1.

…

 As mentioned in  blog post 1 we have improved the capabilities to handle

very large write bandwidth and blog post 2 improved the checkpointing of

disk data columns in MySQL Cluster 8.0.20.

We wanted to verify that these changes were succesful. To do this we

selected to use the YCSB benchmark. This is a very simple benchmark,

it contains one table with in our variant 2 columns. The first

column is the primary key, the second key contains the payload data

and is stored in VARCHAR(29500) column that is stored on disk.

The first part of the benchmark fills the database. The database is

MySQL is an open-source relational database system that works on many Operating Systems, including Windows, Linux, macOS, and FreeBSD. It is probably the most popular OpenSource RDBMS and a central component of the LAMP and LEMP Stacks.

In MySQL Cluster 8.0.20 we added one more query type that can be pushed

down to NDB data nodes. These queries are outer join queries and semi-join

queries using the FirstMatch strategy.

These queries can be pushed if conditions can be pushed on those parts of

the query that involve the outer join using a concept in the MySQL Server

called join nests.

Pushed queries means that the join execution is pushed down to the NDB

data nodes. This means that the rows to process for the second table in

the join is sent directly from the LDM threads to the join processor

that will start the select on the second table. The join can contain

up to 32 tables. Many filters can be pushed as well. It is also possible

to push parts of a join query. We are actively working on supporting

more and more query variants …

 CPU spinning is something that has been around in NDB for a long time. This CPU

spinning has been static which means that each time a thread wakes up it will spin

waiting for incoming signals. Thus if spin time is set to e.g. 500 microsecond we will

spin 5% of the time even when there is no activity since each thread wakes up each

10 ms to perform timer based activities.

In the new spinning approach we wanted to achieve the following.

- Power efficient CPU spinning

  NDB Cluster data nodes can execute queries very efficiently, this leads to efficient

  power usage. However CPU spinning requires careful control to ensure that one

  doesn't  waste power while spinning.

- No spinning unless there is something to gain from it

  Static spinning will always spin independent …